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Jan Zelinka and Jan Romportl and Luděk Müller : A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks . Lecture Notes in Artificial Intelligence, vol. 6231, p. 472-479, Springer, Heidelberg, 2010.

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Abstrakt

The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself.We call it "a priori" because the processed data set does not originate from any measurement or other observation.Machine learning which deals with any observation is called "posterior". The paper describes how posterior machine learning can be modified by a priori machine learning. A priori and posterior machine learning algorithms are proposed for artificial neural network training and are tested in the task of audio-visual phoneme classification.

Detail publikace

Název: A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks
Autor: Jan Zelinka ; Jan Romportl ; Luděk Müller
Název - česky: Apriorní a aposteriorní Machine Learning a ANN
Jazyk publikace: anglicky
Datum vydání: 1.9.2010
Rok vydání: 2010
Typ publikace: Článek z časopisu
Název časopisu / knihy: Lecture Notes in Artificial Intelligence
Číslo vydání: 6231
Strana: 472 - 479
ISSN: 0302-9743
Nakladatel: Springer
Místo vydání: Heidelberg
/ 2011-01-30 16:59:45 /

Klíčová slova

ANN, Machine Learning

Klíčová slova v češtině

umělé neuronové sítě, strojové učení

BibTeX

@ARTICLE{JanZelinka_2010_APrioriandA,
 author = {Jan Zelinka and Jan Romportl and Lud\v{e}k M\"{u}ller},
 title = {A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks},
 year = {2010},
 publisher = {Springer},
 journal = {Lecture Notes in Artificial Intelligence},
 address = {Heidelberg},
 volume = {6231},
 pages = {472-479},
 ISSN = {0302-9743},
 url = {http://www.kky.zcu.cz/en/publications/JanZelinka_2010_APrioriandA},
}